The time-varying and space-varying characteristics of the marine sound field environment, the multi-source nature of the sound mechanism of underwater acoustic targets, and interference from other noise sources have brought many difficulties to the detection and identification of acoustic targets. Conventional target recognition methods are mainly based on the audio time-frequency domain analysis, it is difficult to obtain effective features and robust recognition effects. In order to solve these problems, transfer learning based acoustic target recognition is proposed. The pre-trained networks VGG and VGGish are used to extract deep acoustic feature analysis and fine-tune respectively. Experiments show that the proposed algorithm effectively improves the recognition accuracy and reduces the training time. The fine-tuned transfer learning algorithm has an average accuracy rate of 92.48% in acoustic target recognition, which achieved the state-of-the-art recognition result.